On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data

Detalhes bibliográficos
Autor(a) principal: Patrício, André
Data de Publicação: 2023
Outros Autores: Costa, Rafael S., Henriques, Rui
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/156076
Resumo: Publisher Copyright: © 2023, The Author(s).
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spelling On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic dataHodgkin’s lymphomaCancerMachine learningGene expressionData modelingDiscriminative patternsBiclusteringComputational biologyGeneticsGenetics(clinical)SDG 3 - Good Health and Well-beingPublisher Copyright: © 2023, The Author(s).Background: Despite the advancements in multiagent chemotherapy in the past years, up to 10% of Hodgkin’s Lymphoma (HL) cases are refractory to treatment and, after remission, patients experience an elevated risk of death from all causes. These complications are dependent on the treatment and therefore an increase in the prognostic accuracy of HL can help improve these outcomes and control treatment-related toxicity. Due to the low incidence of this cancer, there is a lack of works comprehensively assessing the predictability of treatment response, especially by resorting to machine learning (ML) advances and high-throughput technologies. Methods: We present a methodology for predicting treatment response after two courses of Adriamycin, Bleomycin, Vinblastine and Dacarbazine (ABVD) chemotherapy, through the analysis of gene expression profiles using state-of-the-art ML algorithms. We work with expression levels of tumor samples of Classical Hodgkin’s Lymphoma patients, obtained through the NanoString’s nCounter platform. The presented approach combines dimensionality reduction procedures and hyperparameter optimization of various elected classifiers to retrieve reference predictability levels of refractory response to ABVD treatment using the regulatory profile of diagnostic tumor samples. In addition, we propose a data transformation procedure to map the original data space into a more discriminative one using biclustering, where features correspond to discriminative putative regulatory modules.Results: Through an ensemble of feature selection procedures, we identify a set of 14 genes highly representative of the result of an fuorodeoxyglucose Positron Emission Tomography (FDG-PET) after two courses of ABVD chemotherapy. The proposed methodology further presents an increased performance against reference levels, with the proposed space transformation yielding improvements in the majority of the tested predictive models (e.g. Decision Trees show an improvement of 20pp in both precision and recall).Conclusions: Taken together, the results reveal improvements for predicting treatment response in HL disease by resorting to sophisticated statistical and ML principles. This work further consolidates the current hypothesis on the structural difficulty of this prognostic task, showing that there is still a considerable gap to be bridged for these technologies to reach the necessary maturity for clinical practice.LAQV@REQUIMTEDQ - Departamento de QuímicaRUNPatrício, AndréCosta, Rafael S.Henriques, Rui2023-07-31T22:18:36Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13application/pdfapplication/pdfhttp://hdl.handle.net/10362/156076eng1755-8794PURE: 66976680https://doi.org/10.1186/s12920-023-01508-9info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:38:38Zoai:run.unl.pt:10362/156076Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:56:18.935888Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data
title On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data
spellingShingle On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data
Patrício, André
Hodgkin’s lymphoma
Cancer
Machine learning
Gene expression
Data modeling
Discriminative patterns
Biclustering
Computational biology
Genetics
Genetics(clinical)
SDG 3 - Good Health and Well-being
title_short On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data
title_full On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data
title_fullStr On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data
title_full_unstemmed On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data
title_sort On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data
author Patrício, André
author_facet Patrício, André
Costa, Rafael S.
Henriques, Rui
author_role author
author2 Costa, Rafael S.
Henriques, Rui
author2_role author
author
dc.contributor.none.fl_str_mv LAQV@REQUIMTE
DQ - Departamento de Química
RUN
dc.contributor.author.fl_str_mv Patrício, André
Costa, Rafael S.
Henriques, Rui
dc.subject.por.fl_str_mv Hodgkin’s lymphoma
Cancer
Machine learning
Gene expression
Data modeling
Discriminative patterns
Biclustering
Computational biology
Genetics
Genetics(clinical)
SDG 3 - Good Health and Well-being
topic Hodgkin’s lymphoma
Cancer
Machine learning
Gene expression
Data modeling
Discriminative patterns
Biclustering
Computational biology
Genetics
Genetics(clinical)
SDG 3 - Good Health and Well-being
description Publisher Copyright: © 2023, The Author(s).
publishDate 2023
dc.date.none.fl_str_mv 2023-07-31T22:18:36Z
2023
2023-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/156076
url http://hdl.handle.net/10362/156076
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1755-8794
PURE: 66976680
https://doi.org/10.1186/s12920-023-01508-9
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 13
application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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